Optimization is a complicated task because it ultimately requires
understanding of the whole system. While it may be possible to do some
local optimizations with small knowledge of your system/application, the
more optimal you want your system to become the more you will have to
know about it.

So this chapter will try to explain and give some examples of different
ways to optimize MySQL. But remember that there are always some
(increasingly harder) additional ways to make the system even faster.

The most important part for getting a system fast is of course the basic
design. You also need to know what kinds of things your system will be
doing, and what your bottlenecks are.

The most common bottlenecks are:

Disk seeks.
It takes time for the disk to find a piece of data. With modern disks in
1999, the mean time for this is usually lower than 10ms, so we can in
theory do about 1000 seeks a second. This time improves slowly with new
disks and is very hard to optimize for a single table. The way to
optimize this is to spread the data on more than one disk.

Disk reading/writing.
When the disk is at the correct position we need to read the data. With
modern disks in 1999, one disk delivers something like 10-20Mb/s. This
is easier to optimize than seeks because you can read in parallel from
multiple disks.

CPU cycles.
When we have the data in main memory (or if it already were
there) we need to process it to get to our result. Having small
tables compared to the memory is the most common limiting
factor. But then, with small tables speed is usually not the problem.

Memory bandwidth.
When the CPU needs more data than can fit in the CPU cache the main
memory bandwidth becomes a bottleneck. This is an uncommon bottleneck
for most systems, but one should be aware of it.

We start with the system level things since some of these decisions have
to be made very early. In other cases a fast look at this part may
suffice because it not that important for the big gains. However, it is always
nice to have a feeling about how much one could gain by changing things
at this level.

The default OS to use is really important! To get the most use of
multiple CPU machines one should use Solaris (because the threads works
really nice) or Linux (because the 2.2 kernel has really good SMP
support). Also on 32-bit machines Linux has a 2G file size limit by
default. Hopefully this will be fixed soon when new filesystems are
released (XFS/Reiserfs). If you have a desperate need for files bigger
than 2G on Linux-intel 32 bit, you should get the LFS patch for the ext2
file system.

Because we have not run MySQL in production on that many platforms, we
advice you to test your intended platform before choosing it, if possible.

Other tips:

If you have enough RAM, you could remove all swap devices. Some
operating systems will use a swap device in some contexts even if you
have free memory.

Use the --skip-lockingMySQL option to avoid external
locking. Note that this will not impact MySQL's functionality as
long as you only run one server. Just remember to take down the server (or
lock relevant parts) before you run myisamchk. On some system
this switch is mandatory because the external locking does not work in any
case.
The --skip-locking option is on by default when compiling with
MIT-pthreads, because flock() isn't fully supported by
MIT-pthreads on all platforms. It's also on default for Linux
as Linux file locking are not yet safe.
The only case when you can't use --skip-locking is if you run
multiple MySQLservers (not clients) on the same data,
or run myisamchk on the table without first flushing and locking
the mysqld server tables first.
You can still use LOCK TABLES/UNLOCK TABLES even if you
are using --skip-locking

Most of the following tests are done on Linux with the
MySQL benchmarks, but they should give some indication for
other operating systems and workloads.

You get the fastest executable when you link with -static.

On Linux, you will get the fastest code when compiling with pgcc
and -O6. To compile `sql_yacc.cc' with these options, you
need about 200M memory because gcc/pgcc needs a lot of memory to
make all functions inline. You should also set CXX=gcc when
configuring MySQL to avoid inclusion of the libstdc++
library (it is not needed). Note that with some versions of pgcc,
the resulting code will only run on true Pentium processors, even if you
use the compiler option that you want the resulting code to be working on
all x586 type processors (like AMD).

By just using a better compiler and/or better compiler options you can
get a 10-30 % speed increase in your application. This is particularly
important if you compile the SQL server yourself!

We have tested both the Cygnus CodeFusion and Fujitsu compilers, but
when we tested them, neither was sufficiently bug free to allow
MySQL to be compiled with optimizations on.

When you compile MySQL you should only include support for the
character sets that you are going to use. (Option --with-charset=xxx).
The standard MySQL binary distributions are compiled with support
for all character sets.

Here is a list of some mesurements that we have done:

If you use pgcc and compile everything with -O6, the
mysqld server is 1% faster than with gcc 2.95.2.

If you link dynamically (without -static), the result is 13%
slower on Linux. Note that you still can use a dynamic linked
MySQL library. It is only the server that is critical for
performance.

If you connect using TCP/IP rather than Unix sockets, the result is 7.5%
slower on the same computer. (If you are connection to localhost,
MySQL will, by default, use sockets).

If you compile with --with-debug=full, then you will loose 20 %
for most queries, but some queries may take substantially longer (The
MySQL benchmarks ran 35 % slower)
If you use --with-debug, then you will only loose 15 %.

The MySQL-Linux distribution provided by MySQL AB used to be
compiled with pgcc, but we had to go back to regular gcc because
of a bug in pgcc that would generate the code that does not run
on AMD. We will continue using gcc until that bug is resolved. In the
meantime, if you have a non-AMD machine, you can get a faster binary by
compiling with pgcc. The standard MySqL Linux binary is linked
statically to get it faster and more portable.

As mentioned before, disks seeks are a big performance bottleneck. This
problems gets more and more apparent when the data starts to grow so
large that effective caching becomes impossible. For large databases,
where you access data more or less randomly, you can be sure that you
will need at least one disk seek to read and a couple of disk seeks to
write things. To minimize this problem, use disks with low seek times.

Increase the number of available disk spindles (and thereby reduce
the seek overhead) by either symlink files to different disks or striping
the disks.

Using symbolic links

This means that you symlink the index and/or data file(s) from the
normal data directory to another disk (that may also be striped). This
makes both the seek and read times better (if the disks are not used for
other things). See section 12.2.2.1 Using Symbolic Links for Databases and Tables.

Striping

Striping means that you have many disks and put the first block on the
first disk, the second block on the second disk, and the Nth on the
(N mod number_of_disks) disk, and so on. This means if your normal data
size is less than the stripe size (or perfectly aligned) you will get
much better performance. Note that striping is very dependent on the OS
and stripe-size. So benchmark your application with different
stripe-sizes. See section 12.7 Using Your Own Benchmarks.
Note that the speed difference for striping is very dependent
on the parameters. Depending on how you set the striping parameters and
number of disks you may get a difference in orders of magnitude. Note that
you have to choose to optimize for random or sequential access.

For reliability you may want to use RAID 0+1 (striping + mirroring), but
in this case you will need 2*N drives to hold N drives of data. This is
probably the best option if you have the money for it! You may, however,
also have to invest in some volume-management software to handle it
efficiently.

A good option is to have semi-important data (that can be regenerated)
on RAID 0 disk while storing really important data (like host information
and logs) on a RAID 0+1 or RAID N disk. RAID N can be a problem if you
have many writes because of the time to update the parity bits.

You may also set the parameters for the file system that the database
uses. One easy change is to mount the file system with the noatime
option. That makes it skip the updating of the last access time in the
inode and by this will avoid some disk seeks.

On Linux, you can get much more performance (up to 100 % under load is
not uncommon) by using hdpram to configure your disk's interface! The
following should be quite good hdparm options for MySQL (and
probably many other applications):

hdparm -m 16 -d 1

Note that the performance/reliability when using the above depends on
your hardware, so we strongly suggest that you test your system
throughly after using hdparm! Please consult the hdparm
man page for more information! If hdparm is not used wisely,
filesystem corruption may result. Backup everything before experimenting!

On many operating systems you can mount the disks with the 'async' flag to set the file
system to be updated asynchronously. If your computer is reasonable stable,
this should give you more performance without sacrificing too much reliability.
(This flag is on by default on Linux.)

If you don't need to know when a file was last accessed (which is not
really useful on a databasa server), you can mount your file systems
with the noatime flag.

You can move tables and databases from the database directory to other
locations and replace them with symbolic links to the new locations.
You might want to do this, for example, to move a database to a file
system with more free space.

If MySQL notices that a table is symbolically linked, it will
resolve the symlink and use the table it points to instead. This works
on all systems that support the realpath() call (at least Linux
and Solaris support realpath())! On systems that don't support
realpath(), you should not access the table through the real path
and through the symlink at the same time! If you do, the table will be
inconsistent after any update.

MySQL doesn't that you link one directory to multiple
databases. Replacing a database directory with a symbolic link will
work fine as long as you don't make a symbolic link between databases.
Suppose you have a database db1 under the MySQL data
directory, and then make a symlink db2 that points to db1:

shell> cd /path/to/datadir
shell> ln -s db1 db2

Now, for any table tbl_a in db1, there also appears to be
a table tbl_a in db2. If one thread updates db1.tbl_a
and another thread updates db2.tbl_a, there will be problems.

If you really need this, you must change the following code in
`mysys/mf_format.c':

MySQL uses algorithms that are very scalable, so you can usually
run with very little memory. If you, however, give MySQL more
memory, you will normally also get better performance.

When tuning a MySQL server, the two most important variables to use
are key_buffer_size and table_cache. You should first feel
confident that you have these right before trying to change any of the
other variables.

If you have much memory (>=256M) and many tables and want maximum performance
with a moderate number of clients, you should use something like this:

When you have installed MySQL, the `support-files' directory will
contain some different my.cnf example files, `my-huge.cnf',
`my-large.cnf', `my-medium.cnf', and `my-small.cnf', you can
use as a base to optimize your system.

If there are very many connections, ``swapping problems'' may occur unless
mysqld has been configured to use very little memory for each
connection. mysqld performs better if you have enough memory for all
connections, of course.

Note that if you change an option to mysqld, it remains in effect only
for that instance of the server.

To see the effects of a parameter change, do something like this:

shell> mysqld -O key_buffer=32m --help

Make sure that the --help option is last; otherwise, the effect of any
options listed after it on the command line will not be reflected in the
output.

table_cache, max_connections, and max_tmp_tables
affect the maximum number of files the server keeps open. If you
increase one or both of these values, you may run up against a limit
imposed by your operating system on the per-process number of open file
descriptors. However, you can increase the limit on many systems.
Consult your OS documentation to find out how to do this, because the
method for changing the limit varies widely from system to system.

table_cache is related to max_connections. For example,
for 200 concurrent running connections, you should have a table cache of
at least 200 * n, where n is the maximum number of tables
in a join.

The cache of open tables can grow to a maximum of table_cache
(default 64; this can be changed with the -O table_cache=#
option to mysqld). A table is never closed, except when the
cache is full and another thread tries to open a table or if you use
mysqladmin refresh or mysqladmin flush-tables.

When the table cache fills up, the server uses the following procedure
to locate a cache entry to use:

Tables that are not currently in use are released, in least-recently-used
order.

If the cache is full and no tables can be released, but a new table needs to
be opened, the cache is temporarily extended as necessary.

If the cache is in a temporarily-extended state and a table goes from in-use
to not-in-use state, the table is closed and released from the cache.

A table is opened for each concurrent access. This means that
if you have two threads accessing the same table or access the table
twice in the same query (with AS) the table needs to be opened twice.
The first open of any table takes two file descriptors; each additional
use of the table takes only one file descriptor. The extra descriptor
for the first open is used for the index file; this descriptor is shared
among all threads.

You can check if your table cache is too small by checking the mysqld
variable opened_tables. If this is quite big, even if you
haven't done a lot of FLUSH TABLES, you should increase your table
cache. See section 7.28.3 SHOW Status Information.

If you have many files in a directory, open, close, and create operations will
be slow. If you execute SELECT statements on many different tables,
there will be a little overhead when the table cache is full, because for
every table that has to be opened, another must be closed. You can reduce
this overhead by making the table cache larger.

MySQL is multithreaded, so it may have many queries on the same
table simultaneously. To minimize the problem with two threads having
different states on the same file, the table is opened independently by
each concurrent thread. This takes some memory and one extra file
descriptor for the data file. The index file descriptor is shared
between all threads.

The list below indicates some of the ways that the mysqld server
uses memory. Where applicable, the name of the server variable relevant
to the memory use is given:

The key buffer (variable key_buffer_size) is shared by all
threads; Other buffers used by the server are allocated as
needed. See section 12.2.3 Tuning Server Parameters.

Each connection uses some thread-specific space: A stack (default 64K,
variable thread_stack), a connection buffer (variable
net_buffer_length), and a result buffer (variable
net_buffer_length). The connection buffer and result buffer are
dynamically enlarged up to max_allowed_packet when needed. When
a query is running, a copy of the current query string is also allocated.

All threads share the same base memory.

Only the compressed ISAM / MyISAM tables are memory mapped. This is
because the 32-bit memory space of 4GB is not large enough for most
big tables. When systems with a 64-bit address space become more
common we may add general support for memory mapping.

Each request doing a sequential scan over a table allocates a read buffer
(variable record_buffer).

All joins are done in one pass, and most joins can be done without even
using a temporary table. Most temporary tables are memory-based (HEAP)
tables. Temporary tables with a big record length (calculated as the
sum of all column lengths) or that contain BLOB columns are
stored on disk.
One problem in MySQL versions before Version 3.23.2 is that if a HEAP table
exceeds the size of tmp_table_size, you get the error The
table tbl_name is full. In newer versions this is handled by
automatically changing the in-memory (HEAP) table to a disk-based
(MyISAM) table as necessary. To work around this problem, you can
increase the temporary table size by setting the tmp_table_size
option to mysqld, or by setting the SQL option
SQL_BIG_TABLES in the client program. See section 7.33 SET Syntax. In MySQL Version 3.20, the maximum size of the
temporary table was record_buffer*16, so if you are using this
version, you have to increase the value of record_buffer. You can
also start mysqld with the --big-tables option to always
store temporary tables on disk. However, this will affect the speed of
many complicated queries.

Almost all parsing and calculating is done in a local memory store. No
memory overhead is needed for small items and the normal slow memory
allocation and freeing is avoided. Memory is allocated only for
unexpectedly large strings (this is done with malloc() and
free()).

Each index file is opened once and the data file is opened once for each
concurrently running thread. For each concurrent thread, a table structure,
column structures for each column, and a buffer of size 3 * n is
allocated (where n is the maximum row length, not counting BLOB
columns). A BLOB uses 5 to 8 bytes plus the length of the BLOB
data. The ISAM/MyISAM table handlers will use one extra row
buffer for internal usage.

For each table having BLOB columns, a buffer is enlarged dynamically
to read in larger BLOB values. If you scan a table, a buffer as large
as the largest BLOB value is allocated.

Table handlers for all in-use tables are saved in a cache and managed as a
FIFO. Normally the cache has 64 entries. If a table has been used by two
running threads at the same time, the cache contains two entries for the
table. See section 12.2.4 How MySQL Opens and Closes Tables.

A mysqladmin flush-tables command closes all tables that are not in
use and marks all in-use tables to be closed when the currently executing
thread finishes. This will effectively free most in-use memory.

ps and other system status programs may report that mysqld
uses a lot of memory. This may be caused by thread-stacks on different
memory addresses. For example, the Solaris version of ps counts
the unused memory between stacks as used memory. You can verify this by
checking available swap with swap -s. We have tested
mysqld with commercial memory-leakage detectors, so there should
be no memory leaks.

You can find a discussion about different locking methods in the appendix.
See section I.4 Locking methods.

All locking in MySQL is deadlock-free. This is managed by always
requesting all needed locks at once at the beginning of a query and always
locking the tables in the same order.

The locking method MySQL uses for WRITE locks works as follows:

If there are no locks on the table, put a write lock on it.

Otherwise, put the lock request in the write lock queue.

The locking method MySQL uses for READ locks works as follows:

If there are no write locks on the table, put a read lock on it.

Otherwise, put the lock request in the read lock queue.

When a lock is released, the lock is made available to the threads
in the write lock queue, then to the threads in the read lock queue.

This means that if you have many updates on a table, SELECT
statements will wait until there are no more updates.

To work around this for the case where you want to do many INSERT and
SELECT operations on a table, you can insert rows in a temporary
table and update the real table with the records from the temporary table
once in a while.

MySQL uses table locking (instead of row locking or column
locking) on all table types, except BDB tables, to achieve a very
high lock speed. For large tables, table locking is MUCH better than
row locking for most applications, but there are, of course, some
pitfalls.

For BDB tables, MySQL only uses table locking if you
explicitely lock the table with LOCK TABLES or execute a command that
will modify every row in the table, like ALTER TABLE.

In MySQL Version 3.23.7 and above, you can insert rows into
MyISAM tables at the same time other threads are reading from
the table. Note that currently this only works if there are no holes after
deleted rows in the table at the time the insert is made.

Table locking enables many threads to read from a table at the same
time, but if a thread wants to write to a table, it must first get
exclusive access. During the update, all other threads that want to
access this particular table will wait until the update is ready.

As updates on tables normally are considered to be more important than
SELECT, all statements that update a table have higher priority
than statements that retrieve information from a table. This should
ensure that updates are not 'starved' because one issues a lot of heavy
queries against a specific table. (You can change this by using
LOW_PRIORITY with the statement that does the update or
HIGH_PRIORITY with the SELECT statement.)

Starting from MySQL Version 3.23.7 one can use the
max_write_lock_count variable to force MySQL to
temporary give all SELECT statements, that wait for a table, a
higher priority after a specific number of inserts on a table.

Table locking is, however, not very good under the following senario:

A client issues a SELECT that takes a long time to run.

Another client then issues an UPDATE on a used table. This client
will wait until the SELECT is finished.

Another client issues another SELECT statement on the same table. As
UPDATE has higher priority than SELECT, this SELECT
will wait for the UPDATE to finish. It will also wait for the first
SELECT to finish!

A thread is waiting for something like full disk, in which case all
threads that wants to access the problem table will also be put in a waiting
state until more disk space is made available.

Some possible solutions to this problem are:

Try to get the SELECT statements to run faster. You may have to create
some summary tables to do this.

Start mysqld with --low-priority-updates. This will give
all statements that update (modify) a table lower priority than a SELECT
statement. In this case the last SELECT statement in the previous
scenario would execute before the INSERT statement.

You can give a specific INSERT, UPDATE, or DELETE
statement lower priority with the LOW_PRIORITY attribute.

Start mysqld with a low value for max_write_lock_count to give
READ locks after a certain number of WRITE locks.

You can specify that all updates from a specific thread should be done with
low priority by using the SQL command: SET SQL_LOW_PRIORITY_UPDATES=1.
See section 7.33 SET Syntax.

You can specify that a specific SELECT is very important with the
HIGH_PRIORITY attribute. See section 7.19 SELECT Syntax.

If you have problems with INSERT combined with SELECT,
switch to use the new MyISAM tables as these support concurrent
SELECTs and INSERTs.

If you mainly mix INSERT and SELECT statements, the
DELAYED attribute to INSERT will probably solve your problems.
See section 7.21 INSERT Syntax.

If you have problems with SELECT and DELETE, the LIMIT
option to DELETE may help. See section 7.17 DELETE Syntax.

When a new threads connects to mysqld, mysqld will span a
new thread to handle the request. This thread will first check if the
hostname is in the hostname cache. If not the thread will call
gethostbyaddr_r() and gethostbyname_r() to resolve the
hostname.

If the operating system doesn't support the above thread-safe calls, the
thread will lock a mutex and call gethostbyaddr() and
gethostbyname() instead. Note that in this case no other thread
can resolve other hostnames that is not in the hostname cache until the
first thread is ready.

You can disable DNS host lookup by starting mysqld with
--skip-name-resolve. In this case you can however only use IP
names in the MySQL privilege tables.

If you have a very slow DNS and many hosts, you can get more performance by
either disabling DNS lookop with --skip-name-resolve or by
increasing the HOST_CACHE_SIZE define (default: 128) and recompile
mysqld.

You can disable the hostname cache with --skip-host-cache. You
can clear the hostname cache with FLUSH HOSTS or mysqladmin
flush-hosts.

If you don't want to allow connections over TCP/IP, you can do this
by starting mysqld with --skip-networking.

One of the most basic optimization is to get your data (and indexes) to
take as little space on the disk (and in memory) as possible. This can
give huge improvements because disk reads are faster and normally less
main memory will be used. Indexing also takes less resources if
done on smaller columns.

MySQL supports a lot of different table types and row formats.
Choosing the right table format may give you a big performance gain.
See section 8 MySQL Table Types.

You can get better performance on a table and minimize storage space
using the techniques listed below:

Use the most efficient (smallest) types possible. MySQL has
many specialized types that save disk space and memory.

Use the smaller integer types if possible to get smaller tables. For
example, MEDIUMINT is often better than INT.

Declare columns to be NOT NULL if possible. It makes everything
faster and you save one bit per column. Note that if you really need
NULL in your application you should definitely use it. Just avoid
having it on all columns by default.

If you don't have any variable-length columns (VARCHAR,
TEXT, or BLOB columns), a fixed-size record format is
used. This is faster but unfortunately may waste some space.
See section 8.1.2 MyISAM Table Formats.

The primary index of a table should be as short as possible. This makes
identification of one row easy and efficient.

For each table, you have to decide which storage/index method to
use. See section 8 MySQL Table Types.

Only create the indexes that you really need. Indexes are good for
retrieval but bad when you need to store things fast. If you mostly
access a table by searching on a combination of columns, make an index
on them. The first index part should be the most used column. If you are
ALWAYS using many columns, you should use the column with more duplicates
first to get better compression of the index.

If it's very likely that a column has a unique prefix on the first number
of characters, it's better to only index this prefix. MySQL
supports an index on a part of a character column. Shorter indexes are
faster not only because they take less disk space but also because they
will give you more hits in the index cache and thus fewer disk
seeks. See section 12.2.3 Tuning Server Parameters.

In some circumstances it can be beneficial to split into two a table that is
scanned very often. This is especially true if it is a dynamic
format table and it is possible to use a smaller static format table that
can be used to find the relevant rows when scanning the table.

Indexes are used to find rows with a specific value of one column
fast. Without an index MySQL has to start with the first record
and then read through the whole table until it finds the relevant
rows. The bigger the table, the more this costs. If the table has an index
for the colums in question, MySQL can quickly get a position to
seek to in the middle of the data file without having to look at all the
data. If a table has 1000 rows, this is at least 100 times faster than
reading sequentially. Note that if you need to access almost all 1000
rows it is faster to read sequentially because we then avoid disk seeks.

Find the MAX() or MIN() value for a specific indexed
column. This is optimized by a preprocessor that checks if you are
using WHERE key_part_# = constant on all key parts < N. In this case
MySQL will do a single key lookup and replace the MIN()
expression with a constant. If all expressions are replaced with
constants, the query will return at once:

SELECT MIN(key_part2),MAX(key_part2) FROM table_name where key_part1=10

Sort or group a table if the sorting or grouping is done on a leftmost
prefix of a usable key (for example, ORDER BY key_part_1,key_part_2 ). The
key is read in reverse order if all key parts are followed by DESC.
The index can also be used even if the ORDER BY doesn't match the index
exactly, as long as all the unused index parts and all the extra
are ORDER BY columns are constants in the WHERE clause. The
following queries will use the index to resolve the ORDER BY part:

SELECT * FROM foo ORDER BY key_part1,key_part2,key_part3;
SELECT * FROM foo WHERE column=constant ORDER BY column, key_part1;
SELECT * FROM foo WHERE key_part1=const GROUP BY key_part2;

In some cases a query can be optimized to retrieve values without
consulting the data file. If all used columns for some table are numeric
and form a leftmost prefix for some key, the values may be retrieved
from the index tree for greater speed:

SELECT key_part3 FROM table_name WHERE key_part1=1

Suppose you issue the following SELECT statement:

mysql> SELECT * FROM tbl_name WHERE col1=val1 AND col2=val2;

If a multiple-column index exists on col1 and col2, the
appropriate rows can be fetched directly. If separate single-column
indexes exist on col1 and col2, the optimizer tries to
find the most restrictive index by deciding which index will find fewer
rows and using that index to fetch the rows.

If the table has a multiple-column index, any leftmost prefix of the
index can be used by the optimizer to find rows. For example, if you
have a three-column index on (col1,col2,col3), you have indexed
search capabilities on (col1), (col1,col2), and
(col1,col2,col3).

MySQL can't use a partial index if the columns don't form a
leftmost prefix of the index. Suppose you have the SELECT
statements shown below:

mysql> SELECT * FROM tbl_name WHERE col1=val1;
mysql> SELECT * FROM tbl_name WHERE col2=val2;
mysql> SELECT * FROM tbl_name WHERE col2=val2 AND col3=val3;

If an index exists on (col1,col2,col3), only the first query
shown above uses the index. The second and third queries do involve
indexed columns, but (col2) and (col2,col3) are not
leftmost prefixes of (col1,col2,col3).

MySQL also uses indexes for LIKE comparisons if the argument
to LIKE is a constant string that doesn't start with a wild-card
character. For example, the following SELECT statements use indexes:

mysql> select * from tbl_name where key_col LIKE "Patrick%";
mysql> select * from tbl_name where key_col LIKE "Pat%_ck%";

In the first statement, only rows with "Patrick" <= key_col <
"Patricl" are considered. In the second statement, only rows with
"Pat" <= key_col < "Pau" are considered.

The following SELECT statements will not use indexes:

mysql> select * from tbl_name where key_col LIKE "%Patrick%";
mysql> select * from tbl_name where key_col LIKE other_col;

In the first statement, the LIKE value begins with a wild-card
character. In the second statement, the LIKE value is not a
constant.

Searching using column_name IS NULL will use indexes if column_name
is an index.

MySQL normally uses the index that finds the least number of rows. An
index is used for columns that you compare with the following operators:
=, >, >=, <, <=, BETWEEN, and a
LIKE with a non-wild-card prefix like 'something%'.

Any index that doesn't span all AND levels in the WHERE clause
is not used to optimize the query. In other words: To be able to use an
index, a prefix of the index must be used in every AND group.

The following WHERE clauses use indexes:

... WHERE index_part1=1 AND index_part2=2 AND other_column=3
... WHERE index=1 OR A=10 AND index=2 /* index = 1 OR index = 2 */
... WHERE index_part1='hello' AND index_part_3=5
/* optimized like "index_part1='hello'" */
... WHERE index1=1 and index2=2 or index1=3 and index3=3;
/* Can use index on index1 but not on index2 or index 3 */

These WHERE clauses do NOT use indexes:

... WHERE index_part2=1 AND index_part3=2 /* index_part_1 is not used */
... WHERE index=1 OR A=10 /* Index is not used in both AND parts */
... WHERE index_part1=1 OR index_part2=10 /* No index spans all rows */

Note that in some cases MySQL will not use an index, even if one
would be available. Some of the cases where this happens are:

If the use of the index would require MySQL to access more
than 30 % of the rows in the table. (In this case a table scan is
probably much faster, as this will require us to do much fewer seeks).
Note that if such a query uses LIMIT to only retrieve
part of the rows, MySQL will use an index anyway, as it can
much more quickly find the few rows to return in the result.

First, one thing that affects all queries: The more complex permission
system setup you have, the more overhead you get.

If you do not have any GRANT statements done, MySQL will
optimize the permission checking somewhat. So if you have a very high
volume it may be worth the time to avoid grants. Otherwise more
permission check results in a larger overhead.

If your problem is with some explicit MySQL function, you can
always time this in the MySQL client:

In most cases you can estimate the performance by counting disk seeks.
For small tables, you can usually find the row in 1 disk seek (as the
index is probably cached). For bigger tables, you can estimate that
(using B++ tree indexes) you will need: log(row_count) /
log(index_block_length / 3 * 2 / (index_length + data_pointer_length)) +
1 seeks to find a row.

In MySQL an index block is usually 1024 bytes and the data
pointer is usually 4 bytes. A 500,000 row table with an
index length of 3 (medium integer) gives you:
log(500,000)/log(1024/3*2/(3+4)) + 1 = 4 seeks.

As the above index would require about 500,000 * 7 * 3/2 = 5.2M,
(assuming that the index buffers are filled to 2/3, which is typical)
you will probably have much of the index in memory and you will probably
only need 1-2 calls to read data from the OS to find the row.

For writes, however, you will need 4 seek requests (as above) to find
where to place the new index and normally 2 seeks to update the index
and write the row.

Note that the above doesn't mean that your application will slowly
degenerate by N log N! As long as everything is cached by the OS or SQL
server things will only go marginally slower while the table gets
bigger. After the data gets too big to be cached, things will start to
go much slower until your applications is only bound by disk-seeks
(which increase by N log N). To avoid this, increase the index cache as
the data grows. See section 12.2.3 Tuning Server Parameters.

In general, when you want to make a slow SELECT ... WHERE faster, the
first thing to check is whether or not you can add an index. See section 12.4 How MySQL Uses Indexes. All references between different tables
should usually be done with indexes. You can use the EXPLAIN command
to determine which indexes are used for a SELECT.
See section 7.29 EXPLAIN Syntax (Get Information About a SELECT).

Some general tips:

To help MySQL optimize queries better, run myisamchk
--analyze on a table after it has been loaded with relevant data. This
updates a value for each index part that indicates the average number of
rows that have the same value. (For unique indexes, this is always 1,
of course.). MySQL will use this to decide which index to
choose when you connect two tables with 'a non-constant expression'.
You can check the result from the analyze run by doing SHOW
INDEX FROM table_name and examining the Cardinality column.

To sort an index and data according to an index, use myisamchk
--sort-index --sort-records=1 (if you want to sort on index 1). If you
have a unique index from which you want to read all records in order
according to that index, this is a good way to make that faster. Note,
however, that this sorting isn't written optimally and will take a long
time for a large table!

The following queries are resolved using only the index tree (assuming
the indexed columns are numeric):

mysql> SELECT key_part1,key_part2 FROM tbl_name WHERE key_part1=val;
mysql> SELECT COUNT(*) FROM tbl_name
WHERE key_part1=val1 AND key_part2=val2;
mysql> SELECT key_part2 FROM tbl_name GROUP BY key_part1;

The following queries use indexing to retrieve the rows in sorted
order without a separate sorting pass:

mysql> SELECT ... FROM tbl_name ORDER BY key_part1,key_part2,...
mysql> SELECT ... FROM tbl_name ORDER BY key_part1 DESC,key_part2 DESC,...

The table B is set to be dependent on table A and all tables
that A is dependent on.

The table A is set to be dependent on all tables (except B)
that are used in the LEFT JOIN condition.

All LEFT JOIN conditions are moved to the WHERE clause.

All standard join optimizations are done, with the exception that a table is
always read after all tables it is dependent on. If there is a circular
dependence then MySQL will issue an error.

All standard WHERE optimizations are done.

If there is a row in A that matches the WHERE clause, but there
wasn't any row in B that matched the LEFT JOIN condition,
then an extra B row is generated with all columns set to NULL.

If you use LEFT JOIN to find rows that don't exist in some
table and you have the following test: column_name IS NULL in the
WHERE part, where column_name is a column that is declared as
NOT NULL, then MySQL will stop searching after more rows
(for a particular key combination) after it has found one row that
matches the LEFT JOIN condition.

RIGHT JOIN is implemented analogously as LEFT JOIN.

The table read order forced by LEFT JOIN and STRAIGHT JOIN
will help the join optimizer (which calculates in which order tables
should be joined) to do its work much more quickly, as there are fewer
table permutations to check.

Note that the above means that if you do a query of type:

SELECT * FROM a,b LEFT JOIN c ON (c.key=a.key) LEFT JOIN d (d.key=a.key) WHERE b.key=d.key

MySQL will do a full scan on b as the LEFT
JOIN will force it to be read before d.

The fix in this case is to change the query to:

SELECT * FROM b,a LEFT JOIN c ON (c.key=a.key) LEFT JOIN d (d.key=a.key) WHERE b.key=d.key

In some cases MySQL will handle the query differently when you are
using LIMIT # and not using HAVING:

If you are selecting only a few rows with LIMIT, MySQL
will use indexes in some cases when it normally would prefer to do a
full table scan.

If you use LIMIT # with ORDER BY, MySQL will end the
sorting as soon as it has found the first # lines instead of sorting
the whole table.

When combining LIMIT # with DISTINCT, MySQL will stop
as soon as it finds # unique rows.

In some cases a GROUP BY can be resolved by reading the key in order
(or do a sort on the key) and then calculate summaries until the
key value changes. In this case LIMIT # will not calculate any
unnecessary GROUP BY's.

As soon as MySQL has sent the first # rows to the client, it
will abort the query.

LIMIT 0 will always quickly return an empty set. This is useful
to check the query and to get the column types of the result columns.

The size of temporary tables uses the LIMIT # to calculate how much
space is needed to resolve the query.

Re-create the indexes with myisamchk -r -q
/path/to/db/tbl_name. This will create the index tree in memory before
writing it to disk, which is much faster because it avoids lots of disk
seeks. The resulting index tree is also perfectly balanced.

The main speed difference is that the index buffer is flushed to disk only
once, after all INSERT statements have completed. Normally there would
be as many index buffer flushes as there are different INSERT
statements. Locking is not needed if you can insert all rows with a single
statement.
Locking will also lower the total time of multi-connection tests, but the
maximum wait time for some threads will go up (because they wait for
locks). For example:

If you don't use locking, 2, 3, and 4 will finish before 1 and 5. If you
use locking, 2, 3, and 4 probably will not finish before 1 or 5, but the
total time should be about 40% faster.
As INSERT, UPDATE, and DELETE operations are very
fast in MySQL, you will obtain better overall performance by
adding locks around everything that does more than about 5 inserts or
updates in a row. If you do very many inserts in a row, you could do a
LOCK TABLES followed by an UNLOCK TABLES once in a while
(about each 1000 rows) to allow other threads access to the table. This
would still result in a nice performance gain.
Of course, LOAD DATA INFILE is much faster for loading data.

Update queries are optimized as a SELECT query with the additional
overhead of a write. The speed of the write is dependent on the size of
the data that is being updated and the number of indexes that are
updated. Indexes that are not changed will not be updated.

Also, another way to get fast updates is to delay updates and then do
many updates in a row later. Doing many updates in a row is much quicker
than doing one at a time if you lock the table.

Note that, with dynamic record format, updating a record to
a longer total length may split the record. So if you do this often,
it is very important to OPTIMIZE TABLE sometimes.
See section 7.11 OPTIMIZE TABLE Syntax.

Use persistent connections to the database to avoid the connection
overhead. If you can't use persistent connections and you are doing a
lot of new connections to the database, you may want to change the value
of the thread_cache_size variable. See section 12.2.3 Tuning Server Parameters.

Try to avoid complex SELECT queries on tables that are updated a
lot. This is to avoid problems with table locking.

The new MyISAM tables can insert rows in a table without deleted
rows at the same time another table is reading from it. If this is important
for you, you should consider methods where you don't have to delete rows
or run OPTIMIZE TABLE after you have deleted a lot of rows.

Use ALTER TABLE ... ORDER BY expr1,expr2... if you mostly
retrieve rows in expr1,expr2.. order. By using this option after big
changes to the table, you may be able to get higher performance.

In some cases it may make sense to introduce a column that is 'hashed'
based on information from other columns. If this column is short and
reasonably unique it may be much faster than a big index on many
columns. In MySQL it's very easy to use this extra column:
SELECT * FROM table_name WHERE hash=MD5(concat(col1,col2))
AND col_1='constant' AND col_2='constant'

For tables that change a lot you should try to avoid all VARCHAR
or BLOB columns. You will get dynamic row length as soon as you
are using a single VARCHAR or BLOB column. See section 8 MySQL Table Types.

It's not normally useful to split a table into different tables just
because the rows gets 'big'. To access a row, the biggest performance
hit is the disk seek to find the first byte of the row. After finding
the data most new disks can read the whole row fast enough for most
applications. The only cases where it really matters to split up a table is if
it's a dynamic row size table (see above) that you can change to a fixed
row size, or if you very often need to scan the table and don't need
most of the columns. See section 8 MySQL Table Types.

If you very often need to calculate things based on information from a
lot of rows (like counts of things), it's probably much better to
introduce a new table and update the counter in real time. An update of
type UPDATE table set count=count+1 where index_column=constant
is very fast!
This is really important when you use databases like MySQL that
only have table locking (multiple readers / single writers). This will
also give better performance with most databases, as the row locking
manager in this case will have less to do.

If you need to collect statistics from big log tables, use summary tables
instead of scanning the whole table. Maintaining the summaries should be
much faster than trying to do statistics 'live'. It's much faster to
regenerate new summary tables from the logs when things change
(depending on business decisions) than to have to change the running
application!

If possible, one should classify reports as 'live' or 'statistical',
where data needed for statistical reports are only generated based on
summary tables that are generated from the actual data.

Take advantage of the fact that columns have default values. Insert
values explicitly only when the value to be inserted differs from the
default. This reduces the parsing that MySQL need to do and
improves the insert speed.

In some cases it's convenient to pack and store data into a blob. In this
case you have to add some extra code in your appliction to pack/unpack
things in the blob, but this may save a lot of accesses at some stage.
This is practical when you have data that doesn't conform to a static
table structure.

Normally you should try to keep all data non-redundant (what
is called 3rd normal form in database theory), but you should not be
afraid of duplicating things or creating summary tables if you need these
to gain more speed.

Stored procedures or UDF (user-defined functions) may be a good way to
get more performance. In this case you should, however, always have a way
to do this some other (slower) way if you use some database that doesn't
support this.

You can always gain something by caching queries/answers in your
application and trying to do many inserts/updates at the same time. If
your database supports lock tables (like MySQL and Oracle),
this should help to ensure that the index cache is only flushed once
after all updates.

Use INSERT /*! DELAYED */ when you do not need to know when your
data is written. This speeds things up because many records can be written
with a single disk write.

Use INSERT /*! LOW_PRIORITY */ when you want your selects to be
more important.

Use SELECT /*! HIGH_PRIORITY */ to get selects that jump the
queue. That is, the select is done even if there is somebody waiting to
do a write.

Use the multi-line INSERT statement to store many rows with one
SQL command (many SQL servers supports this).

Use LOAD DATA INFILE to load bigger amounts of data. This is
faster than normal inserts and will be even faster when myisamchk
is integrated in mysqld.

Use AUTO_INCREMENT columns to make unique values.

Use OPTIMIZE TABLE once in a while to avoid fragmentation when
using dynamic table format. See section 7.11 OPTIMIZE TABLE Syntax.

When using a normal Web server setup, images should be stored as
files. That is, store only a file reference in the database. The main
reason for this is that a normal Web server is much better at caching
files than database contents. So it it's much easier to get a fast
system if you are using files.

Use in memory tables for non-critical data that are accessed often (like
information about the last shown banner for users that don't have
cookies).

Columns with identical information in different tables should be
declared identical and have identical names. Before Version 3.23 you
got slow joins otherwise.
Try to keep the names simple (use name instead of
customer_name in the customer table). To make your names portable
to other SQL servers you should keep them shorter than 18 characters.

If you need REALLY high speed, you should take a look at the low-level
interfaces for data storage that the different SQL servers support! For
example, by accessing the MySQLMyISAM directly, you could
get a speed increase of 2-5 times compared to using the SQL interface.
To be able to do this the data must be on the same server as
the application, and usually it should only be accessed by one process
(because external file locking is really slow). One could eliminate the
above problems by introducing low-level MyISAM commands in the
MySQL server (this could be one easy way to get more
performance if needed). By carefully designing the database interface,
it should be quite easy to support this types of optimization.

In many cases it's faster to access data from a database (using a live
connection) than accessing a text file, just because the database is
likely to be more compact than the text file (if you are using numerical
data), and this will involve fewer disk accesses. You will also save
code because you don't have to parse your text files to find line and
column boundaries.

Declaring a table with DELAY_KEY_WRITE=1 will make the updating of
indexes faster, as these are not logged to disk until the file is closed.
The downside is that you should run myisamchk on these tables before
you start mysqld to ensure that they are okay if something killed
mysqld in the middle. As the key information can always be generated
from the data, you should not lose anything by using DELAY_KEY_WRITE.

You should definately benchmark your application and database to find
out where the bottlenecks are. By fixing it (or by replacing the
bottleneck with a 'dummy module') you can then easily identify the next
bottleneck (and so on). Even if the overall performance for your
application is sufficient, you should at least make a plan for each
bottleneck, and decide how to solve it if someday you really need the
extra performance.

For an example of portable benchmark programs, look at the MySQL
benchmark suite. See section 13 The MySQL Benchmark Suite. You
can take any program from this suite and modify it for your needs. By doing this,
you can try different solutions to your problem and test which is really the
fastest solution for you.

It is very common that some problems only occur when the system is very
heavily loaded. We have had many customers who contact us when they
have a (tested) system in production and have encountered load problems. In
every one of these cases so far, it has been problems with basic design
(table scans are NOT good at high load) or OS/Library issues. Most of
this would be a LOT easier to fix if the systems were not
already in production.

To avoid problems like this, you should put some effort into benchmarking
your whole application under the worst possible load! You can use Sasha's
recent hack for this -
super-smack.
As the name suggests, it can bring your system down to its knees if you ask it,
so make sure to use it only on your developement systems.

MySQL keeps row data and index data in separate files. Many (almost
all) other databases mix row and index data in the same file. We believe that
the MySQL choice is better for a very wide range of modern systems.

Another way to store the row data is to keep the information for each
column in a separate area (examples are SDBM and Focus). This will cause a
performance hit for every query that accesses more than one column. Because
this degenerates so quickly when more than one column is accessed,
we believe that this model is not good for general purpose databases.

The more common case is that the index and data are stored together
(like in Oracle/Sybase et al). In this case you will find the row
information at the leaf page of the index. The good thing with this
layout is that it, in many cases, depending on how well the index is
cached, saves a disk read. The bad things with this layout are:

Table scanning is much slower because you have to read through the indexes
to get at the data.

You can't use only the index table to retrieve data for a query.

You lose a lot of space, as you must duplicate indexes from the nodes
(as you can't store the row in the nodes).

Deletes will degenerate the table over time (as indexes in nodes are
usually not updated on delete).

Because MySQL uses extremely fast table locking (multiple readers /
single writers) the biggest remaining problem is a mix of a steady stream of
inserts and slow selects on the same table.

We believe that for a huge number of systems the extremely fast
performance in other cases make this choice a win. This case is usually
also possible to solve by having multiple copies of the table, but it
takes more effort and hardware.

We are also working on some extensions to solve this problem for some
common application niches.

Because all SQL servers implement different parts of SQL, it takes work to
write portable SQL applications. For very simple selects/inserts it is
very easy, but the more you need the harder it gets. If you want an
application that is fast with many databases it becomes even harder!

To make a complex application portable you need to choose a number of
SQL servers that it should work with.

You can use the MySQL crash-me program/web-page
http://www.mysql.com/information/crash-me.php to find functions,
types, and limits you can use with a selection of database
servers. Crash-me now tests far from everything possible, but it
is still comprehensive with about 450 things tested.

For example, you shouldn't have column names longer than 18 characters
if you want to be able to use Informix or DB2.

Both the MySQL benchmarks and crash-me programs are very
database-independent. By taking a look at how we have handled this, you
can get a feeling for what you have to do to write your application
database-independent. The benchmarks themselves can be found in the
`sql-bench' directory in the MySQL source
distribution. They are written in Perl with DBI database interface
(which solves the access part of the problem).

As you can see in these results, all databases have some weak points. That
is, they have different design compromises that lead to different
behavior.

If you strive for database independence, you need to get a good feeling
for each SQL server's bottlenecks. MySQL is VERY fast in
retrieving and updating things, but will have a problem in mixing slow
readers/writers on the same table. Oracle, on the other hand, has a big
problem when you try to access rows that you have recently updated
(until they are flushed to disk). Transaction databases in general are
not very good at generating summary tables from log tables, as in this
case row locking is almost useless.

To get your application really database-independent, you need to define
an easy extendable interface through which you manipulate your data. As
C++ is available on most systems, it makes sense to use a C++ classes
interface to the databases.

If you use some specific feature for some database (like the
REPLACE command in MySQL), you should code a method for
the other SQL servers to implement the same feature (but slower). With
MySQL you can use the /*! */ syntax to add
MySQL-specific keywords to a query. The code inside
/**/ will be treated as a comment (ignored) by most other SQL
servers.

If REAL high performance is more important than exactness, as in some
Web applications, a possibility is to create an application layer that
caches all results to give you even higher performance. By letting
old results 'expire' after a while, you can keep the cache reasonably
fresh. This is quite nice in case of extremely high load, in which case
you can dynamically increase the cache and set the expire timeout higher
until things get back to normal.

In this case the table creation information should contain information
of the initial size of the cache and how often the table should normally
be refreshed.

During MySQL initial development, the features of MySQL were made to fit
our largest customer. They handle data warehousing for a couple of the
biggest retailers in Sweden.

From all stores, we get weekly summaries of all bonus card transactions,
and we are expected to provide useful information for the store owners
to help them find how their advertisement campaigns are affecting their
customers.

The data is quite huge (about 7 million summary transactions per month),
and we have data for 4-10 years that we need to present to the users.
We got weekly requests from the customers that they want to get
'instant' access to new reports from this data.

We solved this by storing all information per month in compressed
'transaction' tables. We have a set of simple macros (script) that
generates summary tables grouped by different criteria (product group,
customer id, store ...) from the transaction tables. The reports are
Web pages that are dynamically generated by a small Perl script that
parses a Web page, executes the SQL statements in it, and inserts the
results. We would have used PHP or mod_perl instead but they were
not available at that time.

For graphical data we wrote a simple tool in C that can produce
GIFs based on the result of a SQL query (with some processing of the
result). This is also dynamically executed from the Perl script that
parses the HTML files.

In most cases a new report can simply be done by copying an existing
script and modifying the SQL query in it. In some cases, we will need to
add more fields to an existing summary table or generate a new one, but
this is also quite simple, as we keep all transactions tables on disk.
(Currently we have at least 50G of transactions tables and 200G of other
customer data.)

We also let our customers access the summary tables directly with ODBC
so that the advanced users can themselves experiment with the data.

We haven't had any problems handling this with quite modest Sun Ultra
SPARCstation (2x200 Mhz). We recently upgraded one of our servers to a 2
CPU 400 Mhz UltraSPARC, and we are now planning to start handling
transactions on the product level, which would mean a ten-fold increase
of data. We think we can keep up with this by just adding more disk to
our systems.

We are also experimenting with Intel-Linux to be able to get more CPU
power cheaper. Now that we have the binary portable database format (new
in Version 3.23), we will start to use this for some parts of the application.

Our initial feelings are that Linux will perform much better on
low-to-medium load and Solaris will perform better when you start to get a
high load because of extreme disk IO, but we don't yet have anything
conclusive about this. After some discussion with a Linux Kernel
developer, this might be a side effect of Linux giving so much resources
to the batch job that the interactive performance gets very low. This
makes the machine feel very slow and unresponsive while big batches are
going. Hopefully this will be better handled in future Linux Kernels.